{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sdm as sdmlib\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from collections import defaultdict, OrderedDict\n",
    "from IPython.display import clear_output\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Timer(object):\n",
    "    def __init__(self):\n",
    "        self.stats = OrderedDict()\n",
    "        self.current_stats = None\n",
    "        \n",
    "    def start(self, key, name):\n",
    "        self.current_stats = {\n",
    "            'name': name,\n",
    "            't0_loops': [],\n",
    "            't0': time.time(),\n",
    "        }\n",
    "        self.stats[key] = self.current_stats\n",
    "        self.t0 = time.time()\n",
    "        \n",
    "    def measure_loop(self):\n",
    "        self.current_stats['t0_loops'].append(time.time())\n",
    "        \n",
    "    def end(self):\n",
    "        d = self.current_stats\n",
    "        d['dt'] = time.time() - d['t0']\n",
    "        if d['t0_loops']:\n",
    "            d['number-of-loops'] = len(d['t0_loops'])\n",
    "            d['loops-per-second'] = d['number-of-loops'] / d['dt']\n",
    "            d['loops'] = []\n",
    "            \n",
    "            t0 = d['t0']\n",
    "            for t1 in d['t0_loops']:\n",
    "                d['loops'].append(t1-t0)\n",
    "                t0 = t1\n",
    "        d.pop('t0')\n",
    "        d.pop('t0_loops')\n",
    "        self.current_stats = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 - Compare scanners"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_part1(bits, sample, radius, nlinear=1000, nthread=5000, nopencl=5000):\n",
    "    address_space = sdmlib.AddressSpace.init_random(bits, sample)\n",
    "    address_space.opencl_init();\n",
    "    \n",
    "    timer = Timer()\n",
    "\n",
    "    print('Running linear scan...')\n",
    "    timer.start('scan_linear', 'Linear scan')\n",
    "    for i in range(nlinear):\n",
    "        bs = sdmlib.Bitstring.init_random(bits)\n",
    "        address_space.scan_linear2(bs, radius)\n",
    "        timer.measure_loop()\n",
    "    timer.end()\n",
    "\n",
    "    timer.start('scan_thread', 'Thread scan')\n",
    "    print('Running thread scan...')\n",
    "    for i in range(nthread):\n",
    "        bs = sdmlib.Bitstring.init_random(bits)\n",
    "        address_space.scan_thread2(bs, radius)  \n",
    "        timer.measure_loop()\n",
    "    timer.end()\n",
    "    \n",
    "    timer.start('scan_opencl', 'OpenCL scan')\n",
    "    print('Running OpenCL scan...')\n",
    "    for i in range(nopencl):\n",
    "        bs = sdmlib.Bitstring.init_random(bits)\n",
    "        address_space.scan_opencl2(bs, radius)\n",
    "        timer.measure_loop()\n",
    "    timer.end()\n",
    "    \n",
    "    return timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_part1(bits, timer):\n",
    "    from IPython.display import display, Markdown\n",
    "    v = [['', 'Loops', 'Total time', 'Scans per second', 'Time per scan (ms)']]\n",
    "    v.append(['---', '---:', '---:', '---:', '---:'])\n",
    "    for key in ['scan_linear', 'scan_thread', 'scan_opencl']:\n",
    "        stats = timer.stats[key]\n",
    "        v.append([\n",
    "            stats['name'],\n",
    "            stats['number-of-loops'],\n",
    "            stats['dt'],\n",
    "            stats['loops-per-second'],\n",
    "            1000.0 / stats['loops-per-second'],\n",
    "        ])\n",
    "    txt = '\\n'.join(['|'.join([str(x) for x in row]) for row in v])\n",
    "    display(Markdown(txt))\n",
    "    \n",
    "    latex = '\\\\ \\hfill\\n'.join([' & '.join([str(x) for x in row]) for row in v])\n",
    "    print latex\n",
    "\n",
    "    plt.figure(figsize=(8, 6), dpi=300)\n",
    "    plt.hist([1000*x for x in timer.stats['scan_linear']['loops']], bins='fd', density=True, alpha=0.9, label='Linear scan')\n",
    "    plt.hist([1000*x for x in timer.stats['scan_thread']['loops']], bins='fd', density=True, alpha=0.9, label='Thread scan')\n",
    "    plt.hist([1000*x for x in timer.stats['scan_opencl']['loops']], bins='fd', density=True, alpha=0.9, label='OpenCL scan')\n",
    "    plt.legend()\n",
    "    plt.title('Scan performance ($n={}$ bits)'.format(bits))\n",
    "    plt.xlabel('Scan duration (ms)')\n",
    "    plt.ylabel('Probability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running linear scan...\n",
      "Running thread scan...\n",
      "Running OpenCL scan...\n"
     ]
    }
   ],
   "source": [
    "timer1000 = run_part1(1000, 1000000, 451)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'timer1000' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-a035b0257bcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults_part1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimer1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'timer1000' is not defined"
     ]
    }
   ],
   "source": [
    "results_part1(1000, timer1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running linear scan...\n",
      "Running thread scan...\n",
      "Running OpenCL scan...\n"
     ]
    }
   ],
   "source": [
    "timer256 = run_part1(256, 1000000, 103)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'timer256' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-3-99232e66ec25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresults_part1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m256\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimer256\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'timer256' is not defined"
     ]
    }
   ],
   "source": [
    "results_part1(256, timer256)\n",
    "plt.xlim(2.4, 7.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running linear scan...\n",
      "Running thread scan...\n",
      "Running OpenCL scan...\n"
     ]
    }
   ],
   "source": [
    "timer10k = run_part1(10000, 1000000, 4845, nlinear=100, nthread=500, nopencl=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Scans per second|Time per scan (ms)\n",
       "---|---:|---:|---:|---:\n",
       "Linear scan|100|11.6388039589|8.59194813773|116.388039589\n",
       "Thread scan|500|26.7823820114|18.6689891805|53.5647640228\n",
       "OpenCL scan|1000|12.952370882|77.2059423798|12.952370882"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a492890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part1(10000, timer10k)\n",
    "plt.xlim(7, 150);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2 - Compare scanners read/write"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_write(key, name, timer, bits, sample, radius, scanner_type, n):\n",
    "    address_space = sdmlib.AddressSpace.init_random(bits, sample)\n",
    "    counter = sdmlib.Counter.init_zero(bits, sample)\n",
    "    sdm = sdmlib.SDM(address_space, counter, radius, scanner_type)\n",
    "\n",
    "    timer.start('write_{}'.format(key), '{} write'.format(name))\n",
    "    for _ in range(n):\n",
    "        bs = sdmlib.Bitstring.init_random(bits)\n",
    "        sdm.write(bs, bs)\n",
    "        timer.measure_loop()\n",
    "    timer.end()\n",
    "    \n",
    "    timer.start('read_{}'.format(key), '{} single read'.format(name))\n",
    "    for _ in range(n):\n",
    "        bs = sdmlib.Bitstring.init_random(bits)\n",
    "        sdm.read(bs)\n",
    "        timer.measure_loop()\n",
    "    timer.end()\n",
    "    \n",
    "    return timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_part2(bits, sample, radius, nthread=1000, nopencl=1000):\n",
    "    timer = Timer()\n",
    "    print 'Running thread...'\n",
    "    read_write('thread', 'Thread', timer, bits, sample, radius, sdmlib.SDM_SCANNER_THREAD, n=nthread)\n",
    "    print 'Running OpenCL...'\n",
    "    read_write('opencl', 'OpenCL', timer, bits, sample, radius, sdmlib.SDM_SCANNER_OPENCL, n=nopencl)\n",
    "    return timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_part2(bits, timer, hist_xlim=None):\n",
    "    from IPython.display import display, Markdown\n",
    "    v = [['', 'Loops', 'Total time', 'Operation per second', 'Time per operation (ms)']]\n",
    "    v.append(['---', '---:', '---:', '---:', '---:'])\n",
    "    for key in timer.stats.keys():\n",
    "        stats = timer.stats[key]\n",
    "        v.append([\n",
    "            stats['name'],\n",
    "            stats['number-of-loops'],\n",
    "            stats['dt'],\n",
    "            stats['loops-per-second'],\n",
    "            1000.0 / stats['loops-per-second'],\n",
    "        ])\n",
    "    txt = '\\n'.join(['|'.join([str(x) for x in row]) for row in v])\n",
    "    display(Markdown(txt))\n",
    "\n",
    "    plt.figure(figsize=(8, 6), dpi=300)\n",
    "    for key in ['write_thread', 'write_opencl']:\n",
    "        stats = timer.stats[key]\n",
    "        plt.hist([1000*x for x in stats['loops']], bins='fd', density=True, alpha=0.9, label=stats['name'])\n",
    "    plt.legend()\n",
    "    plt.title('Write performance ($n={}$ bits)'.format(bits))\n",
    "    plt.xlabel('Scan duration (ms)')\n",
    "    plt.ylabel('Probability')\n",
    "    if hist_xlim:\n",
    "        plt.xlim(*hist_xlim)\n",
    "    plt.show()\n",
    "    \n",
    "    plt.figure(figsize=(8, 6), dpi=300)\n",
    "    for key in ['read_thread', 'read_opencl']:\n",
    "        stats = timer.stats[key]\n",
    "        plt.hist([1000*x for x in stats['loops']], bins='fd', density=True, alpha=0.9, label=stats['name'])\n",
    "    plt.legend()\n",
    "    plt.title('Read performance ($n={}$ bits)'.format(bits))\n",
    "    plt.xlabel('Scan duration (ms)')\n",
    "    plt.ylabel('Probability')\n",
    "    if hist_xlim:\n",
    "        plt.xlim(*hist_xlim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running thread...\n",
      "Running OpenCL...\n"
     ]
    }
   ],
   "source": [
    "p2timer1000 = run_part2(1000, 1000000, 451, nthread=1000, nopencl=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Operation per second|Time per operation (ms)\n",
       "---|---:|---:|---:|---:\n",
       "Thread write|1000|13.3464519978|74.9262800457|13.3464519978\n",
       "Thread single read|1000|10.3919007778|96.228786377|10.3919007778\n",
       "OpenCL write|1000|7.90451812744|126.509925574|7.90451812744\n",
       "OpenCL single read|1000|5.25757479668|190.201763869|5.25757479668"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Yz/sUaQYzwUEa33ZF+YoO+5fty/EkSZIkabJ4PyNJkiRpsu1ZlBdm5oMt2nufImnCVup3ANIgi4jZwKZF9U0dDlO232biEUmSppuIWAXYAlgXeIgqu/3WzHygr4FJkqaL8v6j2/uZzSJi1cxc1kVMkqRpJiI2AjYG5lJtU744M//W36gkSVPokKJ82jjte32f4rqLNIOY4CC1th7V+U0jHgLu6HCMW4ryBl1FJEmaTj5PldywalH/cERcApwOfCEzF015ZJKk6aK8/7i5w/63Aw/z6N8PVqBKyivvcyRJM9OTIuI6YPPyhYi4DTgHODEzfz7lkUmSpkREPA/Yq6g+cZxu3d6nlPcj63fYX9IQ84gKqbXVivIDmZkdjnH/OGNKkmau7XlscgNUi0i7Ah8AFkbEByNixakMTJI0bZT3H+X9SUuN+5+l44wpSZq51qFJckPD44B/BE6PiEsj4klTF5YkaSpExDrAl4vqn2bmReN07eo+pUn7WY1dUiXNACY4SK2Vv2Qnsg2rfwyUJHVjNvA+4FcR4e8QSVKnvKeRJA2CpwC/i4gD+x2IJKk3ImIF4JvAJrXqu4G3tdG92/uU8h6l2ZiSpikTHKTWyqdqH5zAGH8vyrMnGIskaXpI4ALg34D9qG4C51D9zpkHvJAq8728sVsAfNedHCRJHfKeRpI0GRZTbT/+auDJVDs5zALWBnYE3gJcVvSZDXwzIsptzCVJw+nfgecWdW/MzJva6NvtfUp5jwLep0gzxkrjN5FmtHJxaeUJjFFuizSRJ6YkSdPDGcC3M/P/jfH6rY2Pn0XEh4HvArvXXn8+8C/Af05qlJKk6cR7GklSr70a+EFmNluMWtL4uBz4fES8ETiOR3+XrAx8OyK2ykx/n0jSkIqItwHvLKqPzczvtTlEt/cpzY6j8PeKNEO4g4PU2n1Fudk56eMpswbLMSVJM0RmXtAiuaFsezOwL/Db4qWjImJOz4OTJE1X3tNIknoqM781RnJDs7ZfBl4JLK9VzwMOm4zYJEmTLyJeCXymqD4ROLKDYbq9T2m2W4P3KdIMYYKD1Fr5C3FORESHY8wdZ0xJkppqPNH0GuDhWvUGwLP7E5EkaQiV9x/l/UlLjfsfExwkSROWmT8GvlFU/1M/YpEkdSciXgCcBNTXSX4MHJqZ2cFQXd2nNGn/sDsDSTOHCQ5Sa4upzkofMYtqYakT84ryHV1FJEmaUTLzr8ApRbUJDpKkdpX3H5t02H9DRh9vuZzqPkmSpE58qig/OSI27EskkqQJiYi9gR8w+v7gl8ArMvORDofr9j6lXHdZ1GF/SUPMBAephcxcCtxYVG/a4TBl+6smHpEkaYY6syhv05coJEnD6Oqi3O39zEKfjJIkdSoz/8ToxawAtu5TOJKkDkXErlQP4NSPkrgAeGm7xxYVen2f4rqLNIOY4CCNr/zFuH2H/bcbZzxJksZzU1Fevy9RSJKGkfczkqRBcXNR9r5GkoZARDwZOB1YrVb9B+B5mXn/BIf1PkXShJngII3vj0X5Ge12jIiNgPm1qoeAK3oQkyRpZnmoKM/qSxSSpGH0F0b/HpnfuE9p1+5Fubw/kiSpXd7XSNKQiYhtqI6hWLtWfSXwnMy8u4uhy/uKXSJipaYtm/M+RZrBTHCQxvezorxvRESbfcsz0s/KzPt6EJMkaWZ5XFH2XEFJUlsy817g3KJ6v3b6Nu579i2qT+1FXJKkGcn7GkkaIhGxGfArYINa9fXAfpnZ1c/wzLwKuLZWNZc2Hy6NiLnA/6kPx2PXcSRNYyY4SOO7AFhcK28BLGiz7+uK8sm9CEiSNOPsUZTLIyskSWrllKJc3qeMZW9g81r5duB3PYlIkjSjRMQmwGZFtfc1kjSgGru+nQlsUqu+BXhWZt7So2kmep/yj4w+LuP3mXlrb0KSNAxMcJDGkZnLgROL6qPH28UhIp4F7Fmruhf4fm+jkyRNdxGxFvCyovrMfsQiSRpa3wXqZ+PuFRH7tOrQuN85uqg+oXF/JElSp8pFq5sy85q+RCJJaiki1qE6lmLLWvUiqp0bru/hVF+j2n1hxEERsd04sa0KHFlUf7WHMUkaAiY4SO35BFA/WuKZwHvGahwR84Dji+rjMnNxs/aSJLXwSWCtWvlB4PQ+xSJJGkKZeQfwuaL6+IjYuEW39wJ71cp3A//e69gkSdNfY7Hq8KL6p/2IRZLUWkSsDvwc2KFWvQR4dmZe2cu5MvPPjH4odGXgpIhYY4zYAvgM8IRa9XVUiRKSZpCV+h2ANAwyc3FEfBT4aK36YxGxKfDhke2PImIF4EXAccCmtba3Ap+aqnglSYMnIo4EfpmZl7TZfiWqBLvySacvZebfeh2fJKl/ImJ3YHaTl3YsyqtGxL5jDHNrZl7RYppjgX/m0fPPNwcuiIi3AadmZjZi2QQ4Cnhj0f8jmfk/LcaXJE1zEbET1fFFX87MBzroczKweq16KfDx3kcoSeqBU4Bdirr/ANZrcS8ylksy865x2hwFvBCY0yjvApwbEW/PzLNHGkXE1sDHgP2L/kdm5kMdxiVpyEXjbxiSxtFIXjgZeEHx0iPAQqonmjZn9FO2UN207ZeZ5096kJKkgRURZ1PtAHQBVXb6mcBVmflw0W5N4HnAEcBOxTDXArtm5p2THrAkacpExA089lzyTp2UmQePM89ewC+AVYuXlgDXU93LbAqsWLx+MvDS9A8IkjTwxkma+2StfDvw6jGGaZo0FxELgLOAO4EfAz8BLi53LG08YftE4PXAG4BViqHenpnHjfuPkSRNuYjo5f/z711PUmgx50HAt4HyWPBFwI3ABsAmTV7/z8x8Ww/ilDRkTHCQOtA43+kE4KA2u9wJHNDOL3FJ0vRWS3Co+ztwM1WS3CPAusB8mh8jdhuwl+fUStL0M1UJDo259gF+AKzT5rjfBg7JzL93EZskaYpM5u+UWoJD6XZgMXAvsBowD1h7jLE/lZnv6jI+SdIk6UeCQ2PeVwBfpXmSXjOfBI4wCVuamZr98VzSGDJzWWa+AjgA+GOLpvcDXwC2N7lBktTCKsCWwFOptuDbgub/f3YasKPJDZKkbmXmr4HtgS8CrbYX/wPwssx8lckNkqRxbEh1VvtuVDs3NEtuuAd4tckNkqRmMvM7VL9Dvg20OnLiXGBBZr7b5AZp5nIHB6kLEbEVsCtVZvrKVFu7Xgmcn5nL+hmbJGmwRMR+VOcE7glsy2O3/y7dB5wOfC4zz53k8CRJM1BEzAaeAWxHdTzFg8AtwO8y86/9jE2SNDGTvIPDusDrgL2Bp9PebkBXAV8Djm/jHHZJkoiINYA9gCcAqwPLqI6qOD8zb+lnbJIGgwkOkiRJUywi5lA9PTsf2IhqG9cVqBLl7gKuAP6UmY/0K0ZJkiRJaiUiNqNafNqUateG2VSLUHcBf6NKmLuzfxFKkiRpOjLBQZIkSZIkSZIkSZIkDbxmZzxLkiRJkiRJkiRJkiQNFBMcJEmSJEmSJEmSJEnSwDPBQZIkSZIkSZIkSZIkDTwTHCRJkiRJkiRJkiRJ0sAzwUGSJEmSJEmSJEmSJA08ExwkSZIkSZIkSZIkSdLAM8FBkiRJkiRJkiRJkiQNPBMcJEmSJEmSJEmSJEnSwDPBQZIkSZIkSZIkSZIkDTwTHCRJkiRJkiRJkiRJ0sAzwUGSJEmSJEmSJEmSJA08ExwkSZIkSZIkSZIkSdLAM8FBkiRJkiRJkiRJkiQNPBMcJEmSJEmSJEmSJEnSwDPBQZIkSZIkSZIkSZIkDTwTHCRJkiRJkiRJkiRJ0sAzwUGSJEmSJEmSJEmSJA08ExwkSZIkSZIkSZIkSdLAM8FBkiRJkiRJkiRJkiQNPBMcJEmSJEmSJEmSJEnSwDPBQZIkSZIkSZIkSZIkDTwTHCRJkiRJkiRJkiRJ0sAzwUGSJEmSJEmSJEmSJA08ExwkSZIkSdJQiYgTIyJrH/P7HdNMExE31N7/G/odzzCKiFkRcUXtfTys3zENmojYLCL+3nh/HomInfodkyRJkqT+WqnfAUiSJEmS1K2ImA08FXgCsDYwF1gK3APcCFwLXJeZy/sWpCSN9lZgu8bn1wL/1cdYBlJmLoyILwFvo3pQ6zjgmf2NSpIkSVI/meAgSZIkSRpKERHAi4A3Afsy/j3uvRFxCXAOcDpwsQkPkvohItYD3l+rOiYzH+pXPAPuI8DrgdnAXhFxQGb+sM8xSZIkSeoTj6iQJEmSJA2diNgM+BXwU+AfaC+Bf3VgAXA0cCHwwsmKTxpkEbGgOOLjA/2OaQY6Aliz8fmNwHf6GMtAy8w7gBNqVcdEhH/TlCRJkmYobwYkSZIkSUMlIrYAzgf2afLyg8DVwEXAZcBNwFi7NMSkBChJLUTEhsBhtapPZ+bD/YpnSHyKR3+Wbw8c1MdYJEmSJPWRCQ6SJEmSpKEREbOAU4F5teoEvgnsCczNzG0zc9fM3CkzN6V6SnoB8Amqc+4ldSkz52dmND7m9zueIfNWYE7j86XAif0LZThk5nXAGbWqI/oViyRJkqT+MsFBkiRJkjRM3kT19O6IZcALMvOfMvM3zZ6Czsz7MvOczDwyM7eiSnY4GXhkSiKWpIaIWBV4Y63qh5m5pF/xDJnja5/vGBHP7FskkiRJkvqmnTNKJUmSJEkaFP9clI/JzNM6GSAzzwHO6V1IktS2A4H1auVv9CuQIXQKcDfVrjwAb8af5ZIkSdKM4w4OkiRJkqShEBHrADvXqpYDX+lTOJI0Ea+ufX4XcFa/Ahk2mfkQ8LNa1YsiYvV+xSNJkiSpP9zBQZIkSZI0LOYV5cWZeedUBxERKwO7ApsB6wNzgHuBhcCfM/PaCYy5FvBEYBtgbWBlYAlwB3BxZi7sTfSj5lwBeDqwI7AucD/wN+DczLyt1/O1GdOGwF5UX+vZwCLgj8AlmZn9iKmXImIeVZLOxlTv+RLgJ5l5a4s+U35tTJWI2JTqGtyQ6qn8/wFuA87PzEWTMN82VN+7G1MdUXMHcFFmXtnrucaYfwPgWbWq05odq9PF+LsAT6D6/lkOXAuclZl3j9NvVWAPYDtgdarEi6uA87qJLyK2pvr5slFj3Iepfs7c0ojtigmMfzLwqsbns4GXAl+faIySJEmSho8JDpIkSZKkYVE+qbviVE4eEf8HeC/VAuWcFu1uAH4IfKlVskNEPAU4CHg21SJgtGh7HXAc8JXMXNpmvAcDJ9SqXpuZJzYSG94MHAls0qRrRsQZwLsy88/tzNWtiNgO+A9gP5p/XRdGxEeA4ztNdIiIExl9tMnmmXlDm30XMPoJ+2My8wMt2tdjOyczFzTqnw+8E1jAY3fTvAX4aTHOpFwbRXx1R0fE0S267p2ZZxdj3UCV5AOwMDPnt5q71m8F4GDgHVSJG80sj4iLgI9m5qltjjsfuL5WdVJmHtx47dnAR4CnjdH3SuA97c7Vhecy+vpue/eGsa7FiFgROAx4C1VyQ+mBiPg88P7MXFaMuTpwFPAmYI0mfRdFxHsz86sdxLkK8Hbg9cCW4zRfGhG/BX6QmV9qc4qzi/ILMMFBkiRJmlE8okKSJEmSNCyWFOV1I2KryZ40IlaPiB8CFwAvpEVyQ8N84F3AmIuCEfEW4FLgCGAnWixgN2xBtYj9+27+zRGxBnAG8DmaJzfQiOU5wO8i4jkTnauDmF4H/AH4B8ZOWtkM+C/gh40dNIZCVP6Talv9fWjj7zD9ujamQmMHi4upvjfGSm6A6n3aDTglIk7r5hiCiDgW+AVjJDc0bNeY630TnadN5ffTOd0MFhFzgdOpvv7Nkhug+nn1buCMiJhd67slcAnVddYsuQGqHWqOj4hPtxnPplQ7rnyc8ZMboNqBYR/gixHR1kNYjZ09rqhV7dtI8pAkSZI0Q5jgIEmSJEkaFtcBy4q6T0TEeAvAExYRmwC/BV7W5OV7qbZxvwi4uklsraw6xnhXN8a7FLipSZvtgfMaW913ahbVQnt9i/w7GnNdTrV1fN0cqoSCzZgkEfEq4CvAKsVL9zRi+hPV+zJif+ALkxXPJPgo1ZP1I5ZSXTOXUB3F0Ew/ro1JFxGbUyUJPbV4aTnV9/bvqY55KT0XOCsi1p7AnB+nWtwfcS/wl8ZczY7A+GBE7N/pPB3Ys/b5ksz8axdjBfBdql1PRtxK9W+7guoIjnLu4+B/j8r4NY8mRSTV1+Dixn9Lb298r44dTJU88Stg2+Kl5VRf10uort+rgPtajdWGS2qfrw08qcvxJEmSJA0RExwkSZIkSUOhsb36mUX1/sCZEbF7r+dr7BTwI2CH4qWfUi0Wrp2Z22Xmrpm5LdURGrtQbYV/YxtT/B34PvAaYLPMXCMzt22Mt3NmbgqsR3WcxK21fo+jSgro1JE8usD6LeDJmblhY64dgXWBQ6iSC0asBhw7gbnGFRFbUO3KUE9QuRZ4MbBuZu6YmU+meg9exqOL368DnjkZMfXY9lRPxwNcAxwIrNO4Zp6WmRtRLcxe3qTvZF0b+zU+3lXUf6P2WrOPy9r8NzfVeDr/O8CmteqHqZ70f3xmbpmZuzSOudgKOL4YYmfgix1Ouxfwnsbnv6M67mOdzHxiZu4CbAjsTbXgXvfZdncT6EQjqaC+a8pfuhzyNVTHM0D13m6fmfMa7+MOVP++Mhno0Ih4EtWRDptSJWV9ENi48TV4emZuSZWkcG7R95MRMatFPOURGYuANwDrZeb8xjW/a2ZuR7VjxFZUR2OcQZVg0Yk/FeVWu3NIkiRJmmaiw6MrJUmSJEnqm4jYAzhvjJcXUi2W/ZbqSeErM3N5F3N9jCopYMSDwMGZ+Z02+s4CdsvMprFGxA7Aosy8o81Y1gZ+SbXQO2KHzLxijC5ExMHACUV1Am/IzHIBud5vT6pz7kceingImNfYGr5nIuI0qqfzR1wK7J2Z94zRfm2qRddmRxtsnpk3jNHvROCf22nbpO8C4Kxa1TGZ+YEW7Zv9keU84HmZ2dZT61N0bSygg3/XGGPcQHV0CMDCRnLCWG0PBz5Zq3oQeHFm/rxFn9dTJcDUvTQzfzpG+/nA9U1eOhE4NDPLHQ1G+q1PdazCxrXql2TmyWPFNhERsS/V12nE1zLzdR30X8Dor9mId2Xmp1r0+xrw2lrVlVRHctxHdV2O9TNqDtVuENvVqlu9/xdRJXhBlaCzU2aWySNjxbgtcHW2+UfKiHghcEqt6nOZ+dZ2+kqSJEkafu7gIEmSJEkaGpn5G+BDY7y8GfB64GvAn4G7I+LMiHhfRHT0hG9ErAOUC2ZvaSe5oRHnQ2MtHDZe/0u7C9iN9ncBr6Da7n3Ewe32r/lsq+SGxlznAT+oVc1i9LEWXWssaNaTGx6gWjxtmtzQiOsu4CVUi6fD4i7g5e0mN0Bfr41JERErAv9aVP/fVskNAJn5FeBLRfXhHU7/e6qEnqbJDY15FvHYnynPbda2S5sX5Zt7MOb3WiU3NBzF6GtjJGHhneP8jHqAzt6XrWufn9VuckNjrqvaTW5ouLUoz++gryRJkqQhZ4KDJEmSJGmoZOb7qRZMl43TdDVgH6ot2C+OiD9HxCER0c698KHA3Fr5vMaCa99k5jVUO1OMeEaHQyxl7OSQ0veK8lM7nGs8ry3Kn8/McY/1yMxr6fyogn76fGbeNtmT9ODamEzPAR5fKy8Ejmuz779RJb+M2CMithurcRPvz8yH2mj3/b1quyoAAA7pSURBVKLc6+sdRr8HAH/rcrwE3j9uo8xbqRI96hZSJYKN51RGJ0c8pUXb2bXP23nPu1G+d+V7K0mSJGkaM8FBkiRJkjR0MvOzVOe9fwG4u81uOwBfBS6KiM3GafvsovzZziKcNPUt+FstNjbzq8y8s822fyzKvV5AXFCUv95B3xN7F8aka2vHjx7p5tqYTM8syl/PzIfb6ZiZ/wOURyLs1ea8dwO/6GCeeoLNZCyYr1mU297VYwyXZ+b/a7Ptn4vyT1rtajGisfPIDbWqTVs0r++qsFdEtGrbrfK9K99bSZIkSdOYCQ6SJEmSpKGUmTdn5mHAhsCLgE9TPan84Dhdd6ZKctiy2YsRsRKwW61qOdByO/1uRMSGEfHWiPhWRPwpIm6PiKURkeUH1VEEI+ZExOyxxm2ifIq7lfKIhJ4tIEbEysBOtao7M7NcgB1TZl4GLOlVPJPoXuDKbgaYwmtjMu1alH/dYf8zi/JuTVs91qWZuXz8Zv+rfs1PxoL5nKK8tMvxLumgbZnYdOkE+67Rot0va5+vCZwVEQdGxKwO5mrXA0V5btNWkiRJkqallfodgCRJkiRJ3cjMv1NtpX4q/O8C+pOA3YF9qbbIX7notgHwo4jYucmTzI9j9ILZ1Y0nmXsqItYDjgVeA6w4wWHWov2F0jJpYUyZeX9E1Kt6uVi+MaO/Hm0nN9T8CdizN+FMmoWZmRPp2IdrYzKVu6Vc3mH/y4pyuzsDtH29N9xf+3wqkkNi/CYtLeqgbZkQMNG+rd6XfwdexaOJHFtQHf2xJCJ+AZwNXAD8ucPEk2a6fe8kSZIkDTF3cJAkSZIkTSuZ+WBmXpKZn83MF1EtqP87UCYy7Agc1GSIdYpypwul42rsHvEH4LVMfAEbYJUO2i7rYp5eLiiuVZTbPTaj2z5T7Z6JdOrTtTGZ1q59vrxxHEQnFrcYr5VurvfJUCYZrNrleN38+3r+3mTmNcABPPa6Xwv4R+CLVMkqiyPiRxHxii52GSn73d+0lSRJkqRpyR0cJEmSJEnTWmbeCRwREecCP2X0ovGrgW8VXVYvyj3dvaGxw8RpwCbFS9cA5wBXA7dQLdotBeq7ALwbeHYv4+mD1YpyufDbjmFY0Hyo0w7T9Nqof7178bUuvz+HRXmsyrD+O8aUmadHxA7A+4BX8tjvdagSVPZvfCyKiA8Bn+twt5Ny3LsnEq8kSZKk4WSCgyRJkiRpRsjMn0XEScAhteo9mjS9tyg3W6TrxpuArWvl24GDM/Pn43WMiNf1OJZ+KBes5zRt1drc8ZsMpel4bdwHrNn4vBdf6/L7c1jcVJQ36ksUkywzbwbeGBHvpDoiaAGwF9WOOeWOJOsDnwWeGRH/2OS4oLFsXJRvnHjEkiRJkoaNR1RIkiRJkmaS7xfl1SJizaKu3EJ/gx7HUB6L8dJ2FrAbyuMzhlH5JPt6Exhj3Q7advJkeGkiC/LdmI7Xxl21z1eIiHaPmBhRXh93NW01+G4oyuUuHdNKZt6fmSdn5jsyc2eq6/P5wBd47NfwZcDhHQxfJjjcMOFAJUmSJA0dExwkSZIkSTPJDU3qykXs2xh9LMU2EdGTXRwiYgVgl1rVHzPztx0MsUMv4uizW4AHa+UnTmCMJ3XQdllRnt1B3/U7aNuVaXxtLCzKO3bYv2xfjjcsLi/K2/Qlij7JzHsy87TMPAx4PHBC0eQdERFtDrdtUS7fW0mSJEnTmAkOkiRJkqSZpNnRBnfWC5n5MFBfWF4B+Icezb8uo4+LvLrdjhGxNTCvR3H0TWY+CFxWq1onItpOcoiIHYG1OpjynqK8YQd9dxm/Sc9M5bWxvByig76durAo79Nh/7J9Od5QyMw7gJtrVRNJ7JkWMvN+4A2MTjh7HLBVm0OUCU6/70FYkiRJkoaECQ6SJEmSpJmkXLC+rbHgXiqPBXhbj+YvF5JX7qDvv/QohkFwdlF+TQd9D+5wrvKJ/6e00ykiZgEv7XCubkzltXF/UZ7MozjOKcqvjoiVmrYsNI6zKL8G5/Ykqv44r/b5GhHxhL5F0meNRLLfFdXtHlezc+3zJcCfehKUJEmSpKFggoMkSZIkaShExHoR8crGVv4T6b8y8Nai+hdjNP8acG+tvGdEvH4i8xbuBB6ulXdrZ7E3InZieiU4lNvTHxYRjx+vU0RsCby5w7kuLcovb7PfW4GNO5yrG1N5bfxPUd68w/6dOAO4sZjrLW32/RCjky/Oy8yrehVYH5Q/b/bqSxSDo0xouGu8DhGxPrBdrepXmflIT6OSJEmSNNBMcJAkSZIkDYvVgG8Bf4qIV0fE7HY7RsSqwDeBHYqXvt6sfWYuAY4rqj8XEQe1Od+siNizybiPMPqp5Y2Aw8cZayvgZGBWO3MPg8y8ktGLvXOAn0TE6mP1aTzN/xNglQ6nuwS4o1beLSJe2apDRDwP+GiH83Rliq+NhcB9tfKzGu9vzzX+XeX30scj4lmt+kXEITw2ceNTvYytD34O1Bfj9+5XIL0UEdtFxBcb12O7fXYBFtSqlgDXtdF1QVH+73bnlCRJkjQ9mOAgSZIkSRo22wPfAG6LiK9ExIERsVGzhhGxcUT8C3AlcGDx8o8z89ct5vkgcGGtvDLwnYj4UUTsHhErFnOtFBE7R8SHgWupnj5vpkyq+FhEfDIiNijGWy8iDqc6X35TIIGrW8Q7bN4CLKuVdwb+EBEvrO9c0EgW2R/4A/CkRvUN7U6SmQ8BJxbVJ0TE4RExt14ZEVtGxGeBU6gSKa5td54emZJrIzOXA2fWqtYCLoyI90TEiyNi3+Kj2+SH4xidvLEKcHpEfKT83o2ILSLiy8DxjD6243uZeXKXcfRVZt4O1H/mPK9xFMqwWwV4E3B1RJwTEW+NiCeWPyOh2oEhIt5Jdf3VXz9pjOOCSi+ufb4M+HE3gUuSJEkaPm2deShJkiRJ0gBaAzi08UFE3AkspnoSeFWqJ+A3GKPv74DXtho8Mx+KiAOodhqo7/ywf+Pj3oi4ieooizWB+Y15R4z1NPIJVMcs7NQoB9WT+u+IiGsa8a9LtZV/fQHwY8A8YJtWcQ+LzPxrRLyJ6v0YWcjekiq54O6IuKFRvzlQ39nheKodC+Z3MN1HgFdRvX9QJat8EvhI4z1fBjwO2KTW5yrgvVS7RkyVqbw2Pg28iEff+62Bj4/Rdm/g7A7GHiUzH2nsmnEWVUIGVF/D/wscGRHXUx2bsT7Nv66X0vnRJIPqG8B+jc/Xpnpvz+hfOD21AtWxGyNHbyyNiFt49OiJDai+/lH0uwZ433iDN5JBnl+rOjUz7+kqYkmSJElDxx0cJEmSJEnD4j7gshavr0u1wLsrsCPNkxuWA18C9mtnYSwzbwF2p1p0L61OtZvErsC2jE5uaDXmQ1RPIV9TvLRCLf6tGL2A/R/AUe2MP0wy8yTgjUD55PaaVF/DJzM6ueFHwGETmOceqsX8xcVLqwBPBJ7G6OSGy4B9qRIKpsxUXhuZeQ7wr8BDEwq28/muo/peurR4aQWqxJZdaJ7ccDqwIDPvavLaMPoBo6/Df+pXIFNgNtX1ukvjYzMem9zwW2DPzLy3jfFeSLXbyIgv9CJISZIkScPFBAdJkiRJ0lDIzMWZuRPVYug7gVN59Mng8fyNapv8nTLzzW0upo3Me3dmvphHn7Qebxv1q4EPA69pMeaNVAt+n2P0MQ2lC4HnZObhmZntxjxMMvMrwFOp3tvlYzS7EXgDcGCb29g3m+dSqmMwvgM8MkazJVRHk+zWSG6ZclN5bWTmfwLbUf2bfw3cCjxAdeRFz2XmzVT/tkOBv7RqSrXLyosz83mdfL8OusxcBny5VvWyiFhrrPZD4nKq5JVPAJcAD7fR5wKqn5G7N47uaMeh9Tkz8+xOgpQkSZI0PcQ0/fuIJEmSJGkGiIigeip4a6qtz9ekemr4AaqjI24FLmssrPZqzrlUi3mbAOtRPU1/D3A91aJbR3NFxGrAnlRPOq8JLAVuAi5sLHbPGBGxEdV7MY/q67gI+CPw+14meDQWlBcAj6d6z++mWnA/PzP/3qt5ujXdr42I2JRqV4oNqY6cuYsqGemCzLyjn7FNpojYkOoImzmNqndm5qf7GFJPNX5G7kCVjLYhMJcq6eFuqn/3HzJzUYdjbg78lUcf1vqnzPxmz4KWJEmSNDRMcJAkSZIkSZKmUEQcC7y7UbwR2DIz29n5YEaKiM/x6PE0VwJPzMyxdnyRJEmSNI15RIUkSZIkSZI0tY6l2vkFqt1nXtHHWAZaRKwPvLZWdbTJDZIkSdLMZYKDJEmSJEmSNIUyczFwTK3q6IiY1a94Bty/8ehxHr/JzB/0MxhJkiRJ/eURFZIkSZIkSdIUayQ0XAZs16h6S2Z+vo8hDZyI2Ay4GlgFWA48LTP/0N+oJEmSJPWTCQ6SJEmSJEmSJEmSJGngeUSFJEmSJEmSJEmSJEkaeCY4SJIkSZIkSZIkSZKkgWeCgyRJkiRJkiRJkiRJGngmOEiSJEmSJEmSJEmSpIFngoMkSZIkSZIkSZIkSRp4JjhIkiRJkiRJkiRJkqSBZ4KDJEmSJEmSJEmSJEkaeCY4SJIkSZIkSZIkSZKkgWeCgyRJkiRJkiRJkiRJGngmOEiSJEmSJEmSJEmSpIFngoMkSZIkSZIkSZIkSRp4JjhIkiRJkiRJkiRJkqSBZ4KDJEmSJEmSJEmSJEkaeCY4SJIkSZIkSZIkSZKkgWeCgyRJkiRJkiRJkiRJGngmOEiSJEmSJEmSJEmSpIFngoMkSZIkSZIkSZIkSRp4JjhIkiRJkiRJkiRJkqSBZ4KDJEmSJEmSJEmSJEkaeCY4SJIkSZIkSZIkSZKkgWeCgyRJkiRJkiRJkiRJGngmOEiSJEmSJEmSJEmSpIFngoMkSZIkSZIkSZIkSRp4JjhIkiRJkiRJkiRJkqSBZ4KDJEmSJEmSJEmSJEkaeCY4SJIkSZIkSZIkSZKkgWeCgyRJkiRJkiRJkiRJGngmOEiSJEmSJEmSJEmSpIFngoMkSZIkSZIkSZIkSRp4JjhIkiRJkiRJkiRJkqSBZ4KDJEmSJEmSJEmSJEkaeCY4SJIkSZIkSZIkSZKkgff/AdP049wd1JLTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129271490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12a010610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part2(1000, p2timer1000, hist_xlim=(0, 23))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running thread...\n",
      "Running OpenCL...\n"
     ]
    }
   ],
   "source": [
    "p2timer256 = run_part2(256, 1000000, 103, nthread=2000, nopencl=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Operation per second|Time per operation (ms)\n",
       "---|---:|---:|---:|---:\n",
       "Thread write|2000|6.56891107559|304.464465569|3.2844555378\n",
       "Thread single read|2000|5.11222410202|391.219156298|2.55611205101\n",
       "OpenCL write|2000|5.28474092484|378.448069346|2.64237046242\n",
       "OpenCL single read|2000|4.29032301903|466.165365901|2.14516150951"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1294d9e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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BHtH828lngVdY8l8aLJP/0syKiLMpFSHPo2zveCZwRb1CsUkoPgN4O7B7dZqrgb0z8/ZpD1iaxyLiHcAHOzx1M3ADsBZlm7pVq+dvp/weemk9UFJ/RcS7gfe1ui7IzD0HFY8030XEwyg/527T4el7KJN1FgMbAdsyeuLOiI9k5lunLcgudbVfgea3zFwWEQcBJwEvbD31EMoN3sntwPNN/EuSJGkIrErnX+5qdwFHAp9MZ1FLkuavv2k+AP4cETdQFoY8SPlj6NZ03mr0ZuDpJv6l6ZeZH4qI+4CPAKu0nnpY89HJlcCzM/Oq6Y5PEgAvq9onDyIISUVm3hwR+wGfY8WqrmsDjx5n+FJKFYB6G52BGHevAmlEZt6fmS8Cng9cPM6h9wLHAbu4UlGSJEmz0IuAd1BKn97VxfEJ/Bp4G7B9Zh5v4l+SpL9YjbJ6+LHAXpRFIp3+3vg9YDeTitLMycz/Ah5D2epq6TiH/h54I/AY36PSzIiIJ1D+/xyxhBX3DZc0wzLzeuBJwAuAs4GJKj7eSdkm/dGZ+aHZ8vciy/5rUiJie2BvYAvKSqlFwOXATzPz/kHGJkmSJHWj2d7qkcD2wJbAupSVUXdTfoFbCFyYmd1MEpAkaU6LiKcABwL7ADtRKkKO5x7gNODjmfnjaQ5P0jgiYl1KxY5HUvYmvgf4I+Vn3SsHGZskSbNVRKwD7EmZ4Lo+sDplIcntlIUil83GLSFN/kuSJEmSJEnqWkSsCexCKfG/GaUU6kqUxSF3AJcBl2bmg4OKUZIkSZqPTP5LkiRJkiRJkiRJkjTkOu3BJUmSJEmSJEmSJEmShojJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmSJEmSJEmShpzJf0mSJEmShkREnBwR2frYetAxzTcRsbD1+i8cdDzDKCJWiYjLWq/j4YOOabaJiK0i4s/N6/NgROw+6JgkSZIkzX4rDzoASZIkSdLkRcQawGOBRwIbAGsBi4G7gOuAq4FrMnPZwIKUpNFeD+zcPL4a+O8BxjIrZea1EfFJ4A2UxTsfA/YbbFSSJEmSZjuT/5IkSZI0ZCIigAOAw4AnM/HvdndHxAXAOcBpwC+dDCBpECJiY+A9ra73ZubSQcUzy70feBWwBrBvRDw/M78+4JgkSZIkzWKW/ZckSZKkIRIRWwFnAN8C/pbuJnWvAywAjgJ+Djx7uuKTZrOIWFBtm/Avg45pHno7sF7z+DrgywOMZVbLzFuAk1pd740I/5YnSZIkaUz+wiBJkiRJQyIitgV+Cuzf4eklwJXA+cAlwPXAWKv7Y1oClKRxRMSmwOGtro9m5gODimdIfITl38t3AV44wFgkSZIkzXIm/yVJkiRpCETEKsCpwBat7gS+AOwDrJWZO2Xm3pm5e2ZuSVlduwD4EGVfbUlTlJlbZ2Y0H1sPOp4h83pgzebxYuDkwYUyHDLzGuD0VtfbBxWLJEmSpNnP5L8kSZIkDYfDKKs+R9wPPCsz/yEzf9Jp9Wxm3pOZ52TmkZm5PWUiwCnAgzMSsSQ1ImJ14DWtrq9n5qJBxTNkTmw93i0i9htYJJIkSZJmtW72hpQkSZIkDd7LqvZ7M/N7vZwgM88BzulfSJLUtYOAjVvtzw8qkCH0beBOSjUXgNfi93JJkiRJHbjyX5IkSZJmuYjYENij1bUMOGFA4UjSZLy09fgO4KxBBTJsMnMp8J1W1wERsc6g4pEkSZI0e7nyX5IkSZJmvy2q9m2ZeftMBxERqwJ7A1sBm1D27r4buBb4TWZePYlzrg88CtgR2ABYFVgE3AL8MjOv7U/0o665EvDXwG7ARsC9wB+AH2fmzf2+XpcxbQrsS/larwHcClwMXJCZOYiY+ikitqBMYNmc8povAr6ZmTeNM2bG742ZEhFbUu7BTSmruf8E3Az8NDNvnYbr7Uh5725O2fbjFuD8zLy839ca4/oPBZ7U6vpep61KpnD+vYBHUt4/y4CrgbMy884Jxq0OPAHYGViHMinhCuDcqcQXETtQvr9s1pz3Acr3mRub2C6bxPlPAV7SPF4DeC7wucnGKEmSJGluMvkvSZIkSbNfvcLzITN58Yj4f8A7Kcm7Ncc5biHwdeCT400EiIi/Al4IPJWSIItxjr0G+BhwQmYu7jLeQ4CTWl0vz8yTm6T/a4EjgYd3GJoRcTrw1sz8TTfXmqqI2Bn4D+ApdP66XhsR7wdO7HUSQESczOjtIrbJzIVdjl3A6JXZ783Mfxnn+HZs52Tmgqb/mcARwAJWrD54I/Ct6jzTcm9U8bUdFRFHjTP0iZl5dnWuhZQJMADXZubW4127NW4l4BDgzZRJDZ0si4jzgQ9k5qldnndr4Petrs9m5iHNc08F3g/sOcbYy4F3dHutKXg6o+/vrlf9j3UvRsRDgMOB11ES/7X7IuITwHsy8/7qnOsA7wIOA9btMPbWiHhnZn66hzhXA94EvArYboLDF0fEz4CvZeYnu7zE2VX7WZj8lyRJklSx7L8kSZIkzX6LqvZGEbH9dF80ItaJiK8D5wHPZpzEf2Nr4K3AmAmziHgdcCHwdmB3xknuNralJHh/NZXPOSLWBU4HPk7nxD9NLE8DfhERT5vstXqI6ZXARcDfMvaEjq2A/wa+3lReGApR/BelVPn+dPH3h0HdGzOhqXzwS8p7Y6zEP5TX6XHAtyPie1Mp7R4RRwM/YIzEf2Pn5lrvnux1ulS/n6a0X31ErAWcRvn6d0r8Q/l+9Tbg9IhYozV2O+ACyn3WKfEPpbLJiRHx0S7j2ZJSqeODTJz4h7Jyf3/g+IjoamFOUxHislbXk5sJEJIkSZL0Fyb/JUmSJGn2uwa4v+r7UERMlBydtIh4OPAz4Hkdnr6bUhr7fODKDrGNZ/Uxzndlc74Lges7HLMLcG5TPrxXq1CS0O2y47c01/o1pRx325qUZPtWTJOIeAlwArBa9dRdTUyXUl6XEQcCx01XPNPgA5QV2SMWU+6ZCyjl7TsZxL0x7SJiG8oEmsdWTy2jvLd/Rdk6o/Z04KyI2GAS1/wgJfE94m7gt821Om0r8L6IOLDX6/Rgn9bjRZn5uymcK4D/oVTLGHET5XO7jLKtQX3tj8Ffth/4EcsnDCTla/DL5t/am5r36tjBlIkFZwA7VU8to3xdL6Dcv1cA94x3ri5c0Hq8AfDoKZ5PkiRJ0hxj8l+SJEmSZrmmZPWZVfeBwJkR8fh+X69ZYf4NYNfqqW9REmkbZObOmbl3Zu5E2ZZgL0p58eu6uMSfga8CBwNbZea6mblTc749MnNLYGNKif72nvAPoyTMe3Uky5OPXwQek5mbNtfajbIH/SsoifcRawNHT+JaE4qIbSmr+duTN64GngNslJm7ZeZjKK/B81ieGH4lsN90xNRnu1BWVQNcBRwEbNjcM3tm5maUpOWvO4ydrnvjKc3HW6v+z7ee6/RxSZefc0fNqu4vA1u2uh+grBB/RGZul5l7NVsHbA+cWJ1iD+D4Hi+7L/CO5vEvKFsobJiZj8rMvYBNgSdSktFtx3a7Cr0XTcK9XW3jt1M85cGUkvdQXttdMnOL5nXclfL51RNlDo2IR1PK5G9JmbD0PmDz5mvw15m5HSWB/+Nq7DERsco48dTbDtwKvBrYODO3bu75vTNzZ0qlge0p2w2cTpl80ItLq/Z4VR0kSZIkzUPR45aBkiRJkqQBiIgnAOeO8fS1lETSzygrTC/PzGVTuNa/UxLmI5YAh2Tml7sYuwrwuMzsGGtE7Arcmpm3dBnLBsAPKUnQEbtm5mVjDCEiDgFOqroTeHVm1snV9rh9KPtqj0yUXwps0ZTb7puI+B5lVfeICyl7y981xvEbUBKSncrFb5OZC8cYdzLwsm6O7TB2AR32WR/n+E5/XDgXeEZmdrXaeYbujQX08HmNcY6FlO0YAK5tEvdjHfsW4JhW1xLgOZn5/XHGvIoyOaTtuZn5rTGO3xr4fYenTgYOzcx6JfzIuE0opeo3b3X/XWaeMlZskxERT6Z8nUZ8JjNf2cP4BYz+mo14a2Z+ZJxxnwFe3uq6nLLNwT2U+3Ks71FrUqoI7NzqHu/1P58y+QnK5JXdM7OeWDFWjDsBV2aXf5yLiGcD3251fTwzX9/NWEmSJEnzgyv/JUmSJGkIZOZPgH8d4+mtgFcBnwF+A9wZEWdGxLsjoqeVoRGxIVAnk17XTeK/iXPpWEm15vnfdpvcbY6/A3gRpYT2iEO6Hd9y7HiJ/+Za5wJfa3WtwuitAqasSfa1E//3URKLHRP/TVx3AH9HSSwOizuAF3Sb+IeB3hvTotmP/Y1V9z+Nl/gHyMwTgE9W3W/p8fK/okx26Zj4b65zKyt+T3l6p2OnaJuqfUMfzvmV8RL/jXcx+t4YSeYfMcH3qPvo7XXZofX4rG4T/821rug28d+4qWpv3cNYSZIkSfOAyX9JkiRJGhKZ+R5KMvH+CQ5dG9ifUtb6lxHxm4h4RUR08zvgocBarfa5TTJyYDLzKkpFgxF/0+MpFjP2xInaV6p2vU/7VL28an8iMyfcKiEzr6b38u+D9InMvHm6L9KHe2M6PQ14RKt9Lc3e8134Z8rEkBFPiIidxzq4g/dk5tIujvtq1e73/Q6jXwOAP0zxfAm8Z8KDMm+iTIJou5YySWoipzJ64sBfjXPsGq3H3bzmU1G/dvVrK0mSJGmeM/kvSZIkSUMkM4+l7C99HHBnl8N2BT4NnB8RW01w7FOr9rG9RTht2mXNx0vEdXJGZt7e5bEXV+1+J9cWVO3P9TD25P6FMe26qhTRJ1O5N6bTflX7c5n5QDcDM/NPQF1mft8ur3sn8IMertOefDIdyeT1qnbX1SDG8OvM/L8uj/1N1f7meNUQRjQVKxa2urYc5/D2avx9I2K8Y6eqfu3q11aSJEnSPGfyX5IkSZKGTGbekJmHA5sCBwAfpaxwXTLB0D0oEwC26/RkRKwMPK7VtQwYt0T5VETEphHx+oj4YkRcGhF/jIjFEZH1B6W8+4g1I2KNsc7bQb36dzx12fm+JdciYlVg91bX7ZlZJyfHlJmXAIv6Fc80upuyv/qkzeC9MZ32rto/6nH8mVX7cR2PWtGFmbls4sP+on3PT0cyec2qvXiK57ugh2PrST8XTnLsuuMc98PW4/WAsyLioIhYpYdrdeu+qr1Wx6MkSZIkzVsrDzoASZIkSdLkZOafKeWpT4W/JJcfDTweeDKl7Piq1bCHAt+IiD06rIB9GKOTSVf2smd7tyJiY+Bo4GDgIZM8zfp0n0TsZR/5eyOi3dXPRPLmjP56dJ34b7kU2Kc/4Uyba3vcx/wvBnBvTKe6ysavexx/SdXudkV51/d7497W45mYOBETHzKuW3s4tk6WT3bseK/Lh4GXsHySw7aU7RQWRcQPgLOB84Df9Dgpo5OpvnaSJEmS5jhX/kuSJEnSHJGZSzLzgsw8NjMPoCSbPwzUSf7dgBd2OMWGVbvXJOKEmqoDFwEvZ/LJXYDVejj2/ilcp5/JtvWrdrdbEUx1zEy7azKDBnRvTKcNWo+XNSX2e3HbOOcbz1Tu9+lQJ+BXn+L5pvL59f21ycyrgOez4n2/PvD3wPGUiRy3RcQ3IuJFU6hOUY+7t+NRkiRJkuYtV/5LkiRJ0hzV7HP/9oj4MWX/8HZC9aXAF6sh61Ttvq76byoTfA94ePXUVcA5wJXAjZSE1mKgvXr8bcBT+xnPAKxdteukaDeGIdm3tNcBc/TeaH+9+/G1rt+fw6LeqmJYP48xZeZpEbEr8G7gxaz4XocyeePA5uPWiPhX4OM9Vsmoz3vnZOKVJEmSNHeZ/JckSZKkOS4zvxMRnwVe0ep+QodD767anRJYU3EYsEOr/UfgkMz8/kQDI+KVfY5lEOpkbr0Xejfm6h7fc/HeuIeyBzz052tdvz+HxfVVe7OBRDHNMvMG4DURcQRl25UFwL6USit1JYtNgGOB/SLi7ztswTKWzav2dZOPWJIkSdJcZNl/SZIkSZofvlq1146I9aq+uiz5Q/scQ73VwHO7Se426i0JhlG9AnrjSZxjox6O7WVFcW0yyeqpmIv3xh2txytFRLdl+0fU98cdHY+a/RZW7bq6w5ySmfdm5imZ+ebM3INyfz4TOI4Vv4bPA97Sw+nr5P/CSQcqSZIkaU4y+S9JkiRJ88PCDn11gvdmRpf63zEi+rL6PyJWAvZqdV2cmT/r4RS79iOOAbsRWNJqP2oS53h0D8fW+5v3ss/4Jj0cOyVz+N64tmrv1uP4+vj6fMPi11V7x4FEMSCZeVdmfi8zDwceAZxUHfLmiIguT7dT1a5fW0mSJEnznMl/SZIkSZofOpWLv73dyMwHgHbSdSXgb/t0/Y0YvfXcld0OjIgdgC36FMfAZOYS4JJW14YR0fUEgIjYDVi/h0veVbU37WHsXhMf0jczeW8sq0/Rw9he/bxq79/j+Pr4+nxDITNvAW5odU1m0suckJn3Aq9m9GSshwHbd3mKevLPr/oQliRJkqQ5xOS/JEmSJM0PdTL35iYZXatLrb+hT9evk6yr9jD2H/sUw2xwdtU+uIexh/R4rXql+F91MygiVgGe2+O1pmIm7417q/Z0bm9wTtV+aUSs3PHISrNFQP01+HFfohqMc1uP142IRw4skgFrJln9ourudguQPVqPFwGX9iUoSZIkSXOGyX9JkiRJmuUiYuOIeHFTHn0y41cFXl91/2CMwz8D3N1q7xMRr5rMdSu3Aw+02o/rJhEaEbszt5L/dcnvwyPiERMNiojtgNf2eK0Lq/YLuhz3elbcW3w6zeS98aeqvU2P43txOnBdda3XdTn2Xxk9MeHczLyiX4ENQP39Zt+BRDF71Mn+OyYaEBGbADu3us7IzAf7GpUkSZKkoWfyX5IkSZJmv7WBLwKXRsRLI6LrvdsjYnXgC6y4L/rnOh2fmYuAj1XdH4+IF3Z5vVUiYp8O532Q0atdNwPeMsG5tgdOAVbp5trDIDMvZ3QidE3gmxGxzlhjmlXg3wRW6/FyFwC3tNqPi4gXjzcgIp4BfKDH60zJDN8b1wL3tNpPal7fvms+r/q99MGIeNJ44yLiFaw4qeEj/YxtAL4PtBPVTxxUIP0UETtHxPHN/djtmL2ABa2uRcA1XQxdULW/2+01JUmSJM0fJv8lSZIkaXjsAnweuDkiToiIgyJis04HRsTmEfGPwOXAQdXT/5uZPxrnOu9j9P7iqwJfjohvRMTjI+Ih1bVWjog9IuLfgKspq5Y7qScc/HtEHBMRD63Ot3FEvIWyn/WWQNLDPvBD4HXA/a32HsBFEfHs9or3ZiLFgcBFLJv0qioAAAbbSURBVN/re2G3F8nMpcDJVfdJEfGWiFir3RkR20XEscC3KZMMru72On0yI/dGZi4Dzmx1rQ/8PCLeERHPiYgnVx9TnRjwMUZPbFgNOC0i3l+/dyNi24j4FHAio7dC+EpmnjLFOAYqM/8ItL/nPKPZXmLYrQYcBlwZEedExOsj4lH190goK/cj4gjK/dd+/rNjbMFSe07r8f3A/04lcEmSJElzU1d7zUmSJEmSZpV1gUObDyLiduA2ygrS1Skrpx86xthfAC8f7+SZuTQink9Zod6uGHBg83F3RFxP2R5gPWDr5rojxlrFehKldP3uTTsoK7zfHBFXNfFvRCmP3k6O/TuwBbDjeHEPi8z8XUQcRnk9RpK821ES73dGxMKmfxugXRHgRMpK9617uNz7gZdQXj8oEzmOAd7fvOb3Aw8DHt4acwXwTkq1gZkyk/fGR4EDWP7a7wB8cIxjnwic3cO5R8nMB5tqC2dRJitA+Rr+E3BkRPyeshXBJnT+ul5I79s9zFafB57SPN6A8tqePrhw+molylYGI9sZLI6IG1lezv+hlK9/VOOuAt490cmbiRLPbHWdmpl3TSliSZIkSXOSK/8lSZIkafa7B7hknOc3oiQ/9wZ2o3PifxnwSeAp3SSNMvNG4PGUhHRtHUoVgr2BnRid+B/vnEspq1evqp5aqRX/9oxO7v4H8K5uzj9MMvOzwGuAesXvepSv4WMYnfj/BnD4JK5zFyXRfVv11GrAo4A9GZ34vwR4MiXZPmNm8t7IzHOANwJLJxVs79e7hvJeurB6aiXKpI+96Jz4Pw1YkJkT7gc/JL7G6PvwHwYVyAxYg3K/7tV8bMWKif+fAftk5t1dnO/ZlCoVI47rR5CSJEmS5h6T/5IkSZI0y2XmbZm5OyVReARwKstXlE7kD5TS47tn5mu7TDSNXPfOzHwOy1foTlSa+krg34CDxznndZRk2McZXfq+9nPgaZn5lszMbmMeJpl5AvBYymu7bIzDrgNeDRzUZWnwTte5kLK1wJcZve962yLKdg+PayZ+zLiZvDcy87+AnSmf84+Am4D7KNsI9F1m3kD53A4FfjveoZTqHM/JzGf08n6d7TLzfuBTra7nRcT6Yx0/JH5NmdjxIeAC4IEuxpxH+R75+GY7hG4c2r5mZp7dS5CSJEmS5o+Yo39DkSRJkqQ5LSKCspp0B0o56fUoq03vo5Tjvwm4pEk69uuaa1ESXQ8HNqaswr4L+D0lIdXTtSJibWAfygrZ9YDFwPXAz5tE8LzR7P++D6WE/RrArcDFwK/6OfmhSbYuAB5Bec3vpCSjf5qZf+7XdaZqrt8bEbElpZrBppRtPO6gTNQ5LzNvGWRs0ykiNqVsC7Jm03VEZn50gCH1VfM9clfKRK1NgbUoEwLupHzeF2XmrT2ecxvgdyxfwPMPmfmFvgUtSZIkaU4x+S9JkiRJkqQZERFHA29rmtcB22VmNyvm56WI+DjLt/y4HHhUZo5VKUSSJEnSPGfZf0mSJEmSJM2UoykVQ6BULXnRAGOZ1SJiE+Dlra6jTPxLkiRJGo/Jf0mSJEmSJM2IzLwNeG+r66iIWGVQ8cxy/8zyLRJ+kplfG2QwkiRJkmY/y/5LkiRJkiRpxjTJ/kuAnZuu12XmJwYY0qwTEVsBVwKrAcuAPTPzosFGJUmSJGm2M/kvSZIkSZIkSZIkSdKQs+y/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElDzuS/JEmSJEmSJEmSJElD7v8D7QYQrjQ73JYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129d675d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part2(256, p2timer256, hist_xlim=(0, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running thread...\n"
     ]
    }
   ],
   "source": [
    "#p2timer10k = run_part2(10000, 1000000, 4845, nthread=500, nopencl=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#results_part2(10000, p2timer10k, hist1_xlim=(0, 8), hist2_xlim=(0, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 - Kernels comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_part3(bits, sample, radius, n=1000):\n",
    "    address_space = sdmlib.AddressSpace.init_random(bits, sample)\n",
    "    address_space.opencl_init()\n",
    "    #address_space.opencl_opts.verbose = 1\n",
    "    bs = sdmlib.Bitstring.init_random(bits)\n",
    "    expected = set(address_space.scan_opencl2(bs, radius))\n",
    "    \n",
    "    timer = Timer()\n",
    "    for kernel in sdmlib.OPENCL_KERNEL_NAMES:\n",
    "        address_space.set_opencl_kernel(kernel)\n",
    "        \n",
    "        print('Running kernel {}...'.format(kernel))\n",
    "        timer.start(kernel, kernel)\n",
    "        for _ in range(n):\n",
    "            address_space.scan_opencl2(bs, radius)\n",
    "            timer.measure_loop()\n",
    "        timer.end()\n",
    "    return timer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "def results_part3(bits, timer, hist_xlim=None):\n",
    "    from IPython.display import display, Markdown\n",
    "    v = [['', 'Loops', 'Total time', 'Scans per second', 'Time per scan (ms)']]\n",
    "    v.append([':---', '---:', '---:', '---:', '---:'])\n",
    "    for key in timer.stats.keys():\n",
    "        stats = timer.stats[key]\n",
    "        v.append([\n",
    "            stats['name'],\n",
    "            stats['number-of-loops'],\n",
    "            stats['dt'],\n",
    "            stats['loops-per-second'],\n",
    "            1000.0 / stats['loops-per-second'],\n",
    "        ])\n",
    "    txt = '\\n'.join(['|'.join([str(x) for x in row]) for row in v])\n",
    "    display(Markdown(txt))\n",
    "    \n",
    "    plt.figure(figsize=(8, 6), dpi=300)\n",
    "    for key in timer.stats.keys():\n",
    "        stats = timer.stats[key]\n",
    "        plt.hist([1000*x for x in stats['loops']], bins='fd', density=True, alpha=0.75, label=stats['name'])\n",
    "    plt.legend()\n",
    "    plt.title('Kernels performance ($n={}$ bits)'.format(bits))\n",
    "    plt.xlabel('Scan duration (ms)')\n",
    "    plt.ylabel('Probability')\n",
    "    if hist_xlim:\n",
    "        plt.xlim(*hist_xlim)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running kernel single_scan0...\n",
      "Running kernel single_scan1...\n",
      "Running kernel single_scan2...\n",
      "Running kernel single_scan3...\n",
      "Running kernel single_scan4...\n",
      "Running kernel single_scan5...\n",
      "Running kernel single_scan5_unroll...\n",
      "Running kernel single_scan6...\n"
     ]
    }
   ],
   "source": [
    "p3timer1000 = run_part3(1000, 1000000, 451, n=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Scans per second|Time per scan (ms)\n",
       ":---|---:|---:|---:|---:\n",
       "single_scan0|3000|15.028241872787476|199.62414934459346|5.009413957595825\n",
       "single_scan1|3000|11.874222040176392|252.6481305343213|3.958074013392131\n",
       "single_scan2|3000|13.736932039260864|218.38937482007222|4.578977346420288\n",
       "single_scan3|3000|11.063963174819946|271.1505771121497|3.687987724939982\n",
       "single_scan4|3000|12.131728887557983|247.2854469305467|4.043909629185994\n",
       "single_scan5|3000|11.181737899780273|268.29460920014515|3.7272459665934243\n",
       "single_scan5_unroll|3000|10.841995000839233|276.7018431356759|3.6139983336130777\n",
       "single_scan6|3000|12.084025859832764|248.26163356468672|4.028008619944255"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part3(1000, p3timer1000, hist_xlim=(2, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running kernel single_scan0...\n",
      "Running kernel single_scan1...\n",
      "Running kernel single_scan2...\n",
      "Running kernel single_scan3...\n",
      "Running kernel single_scan4...\n",
      "Running kernel single_scan5...\n",
      "Running kernel single_scan5_unroll...\n",
      "Running kernel single_scan6...\n"
     ]
    }
   ],
   "source": [
    "p3timer256 = run_part3(256, 1000000, 103, n=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Scans per second|Time per scan (ms)\n",
       ":---|---:|---:|---:|---:\n",
       "single_scan0|3000|9.098679065704346|329.7181907764943|3.0328930219014483\n",
       "single_scan1|3000|8.634926080703735|347.4262514770137|2.8783086935679116\n",
       "single_scan2|3000|11.461902141571045|261.7366614149787|3.8206340471903486\n",
       "single_scan3|3000|11.187175273895264|268.1642082608964|3.729058424631755\n",
       "single_scan4|3000|13.442016124725342|223.18080652215394|4.480672041575114\n",
       "single_scan5|3000|11.295457601547241|265.59348950936374|3.76515253384908\n",
       "single_scan5_unroll|3000|11.396361827850342|263.24190520773243|3.798787275950114\n",
       "single_scan6|3000|13.104900121688843|228.92200414675017|4.368300040562947"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part3(256, p3timer256, hist_xlim=(2, 7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running kernel single_scan0...\n",
      "Running kernel single_scan1...\n",
      "Running kernel single_scan2...\n",
      "Running kernel single_scan3...\n",
      "Running kernel single_scan4...\n",
      "Running kernel single_scan5...\n",
      "Running kernel single_scan5_unroll...\n",
      "Running kernel single_scan6...\n"
     ]
    }
   ],
   "source": [
    "p3timer10k = run_part3(10000, 1000000, 4845, n=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Scans per second|Time per scan (ms)\n",
       ":---|---:|---:|---:|---:\n",
       "single_scan0|500|30.534967184066772|16.374669636484867|61.069934368133545\n",
       "single_scan1|500|22.480546236038208|22.24145244293299|44.961092472076416\n",
       "single_scan2|500|22.491918802261353|22.23020651976254|44.983837604522705\n",
       "single_scan3|500|6.335015773773193|78.92640174156905|12.670031547546387\n",
       "single_scan4|500|5.727261781692505|87.30175414685539|11.45452356338501\n",
       "single_scan5|500|6.294461011886597|79.43491890025042|12.588922023773193\n",
       "single_scan5_unroll|500|5.688975811004639|87.88928211521136|11.377951622009277\n",
       "single_scan6|500|5.48170280456543|91.21253337258189|10.96340560913086"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part3(10000, p3timer10k, hist_xlim=(8, 70))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|Loops|Total time|Scans per second|Time per scan (ms)\n",
       ":---|---:|---:|---:|---:\n",
       "single_scan0|500|30.534967184066772|16.374669636484867|61.069934368133545\n",
       "single_scan1|500|22.480546236038208|22.24145244293299|44.961092472076416\n",
       "single_scan2|500|22.491918802261353|22.23020651976254|44.983837604522705\n",
       "single_scan3|500|6.335015773773193|78.92640174156905|12.670031547546387\n",
       "single_scan4|500|5.727261781692505|87.30175414685539|11.45452356338501\n",
       "single_scan5|500|6.294461011886597|79.43491890025042|12.588922023773193\n",
       "single_scan5_unroll|500|5.688975811004639|87.88928211521136|11.377951622009277\n",
       "single_scan6|500|5.48170280456543|91.21253337258189|10.96340560913086"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results_part3(10000, p3timer10k, hist_xlim=(10, 15))"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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